alexa Robustness of Change Detection Algorithms in the Presence of Registration Errors
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Theoretical and Computational Science

Author(s): Ashok Sundaresan, Pramod K, Varshney, Manoj K Arora

Abstract Share this page

images is critical to any change detection study. The presence of registration errors in the images may affect the accuracy of change detection. In this paper, we evaluate the performance of two change detection algorithms in the presence of artificially introduced registration errors in the dataset. The algorithms considered are image differencing and an algorithm based on a Markov random field (MRF) model. Registration errors have been introduced in four different ways: only in x direction, only in y direction, in both x and y directions without any rotational misregistration, and finally in both x and y directions together with rotational misregistration. Three temporal datasets, a simulated dataset and two synthetic datasets created from remote sensing images acquired by the Landsat TM sensor, have been used in our study. The results indicate that the change detection algorithm based on the MRF model is more robust to the presence of registration errors than the image differencing method.

This article was published in PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING and referenced in Journal of Theoretical and Computational Science

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version